Microsoft, Motorola, Siemens, Hitachi, IAPR, NICI, IUF
This paper describes a two-stage classification method for (1) classification of isolated characters and (2) verification of the classification result. Character prototypes are generated using hierarchical clustering. For those prototypes known to sometimes produce wrong classification results, a \\\"support vector classifier\\\" (svc) is trained. The svc can be used to increase the confidence that a classification is correct and furthermore decide on a classification if the confidence using the standard method is too low. Experiments with the iUF UNIPEN database yield 94% recognition rate. In cases where both classifiers agree, the error rate is zero.
